Adaptive active suspension system with road preview

ABSTRACT

A method for controlling an active suspension includes steps of determining a dimension of a road abnormality ahead of the vehicle and comparing the dimension with a vehicle dimension. Responsive to the comparison, the abnormality is classified as one type of a plurality of predetermined types. Responsive to a height dimension of the abnormality, the abnormality is further classified as having one of a small, medium, or large severity. The suspension is controlled responsive to the type and severity.

BACKGROUND OF THE INVENTION

The embodiments of the present invention relate to vehicle suspensioncontrol systems and, more specifically, to a vehicle suspension controlsystem adaptable to predicted and actual abnormal road conditions.

Passenger vehicles are designed to drive on a variety of road surfacesand geometric conditions. Occasionally, a vehicle encounters exceptional(abnormal) road conditions, such as debris, severe potholes, bumps andthe like. Adaptive or active suspension systems enable selectiveadjustment of the suspension characteristics such as damping andstiffness, responsive to the contact between the vehicle wheels and theabnormal road feature. This helps improve the vehicle's ride comfort,handling, and safety.

However, in order for conventional active suspensions to operate theactuatable elements of the suspension system responsive to the abnormalroad feature, the vehicle wheels must contact the road feature. Due tothe need to interact with the abnormal road feature prior to operatingthe actuatable suspension elements, the system response may not be astimely and effective as it might otherwise be, because of factors suchas the dynamic response times of the system as a whole and theindividual system elements, and the speed of the vehicle.

Certain vehicle design parameters may also be tailored to mitigate theeffects of interaction between the vehicle and abnormal road features.For example, providing a vehicle with a relatively higher groundclearance reduces its susceptibility to damage due to many bumps in theroad or debris located on the road surface. However, a vehicle withhigher ground clearance may have a relatively greater fuel consumptionthan a vehicle with a smaller ground clearance.

In view of the above, it is desirable to incorporate an adaptive oractive suspension system into the vehicle to enable selective adjustmentof characteristics such as suspension stiffness and damping responsiveto abnormal road conditions. It is also desirable to have advance noticeof the type and severity of any abnormal road or driving conditions sothat actuatable elements of a vehicle control system can be actuated inreal time to help mitigate the undesirable consequences of driving overthe abnormal surfaces, if necessary before the vehicle wheels encounterthe abnormal road condition.

Thus, it is desirable to detect an abnormal road condition, predict theseverity of the condition, and operate actuatable elements of thesuspension system in response to the predicted type and severity of theabnormal road condition. For efficiency, it is also desirable that thesuspension control system operates the actuatable system elements onlyfor the length of time (and only to the degree) needed to mitigate thedeleterious effects of a particular abnormal road condition.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings illustrating embodiments of the present invention:

FIG. 1 is a schematic diagram of a vehicle control system incorporatingan adaptive active suspension control system in accordance withembodiments of the present invention.

FIG. 2 is a schematic diagram of a portion of an active suspensionsystem controllable in accordance with an embodiment of the presentinvention.

FIG. 3 is a block diagram of an estimator means in accordance with oneembodiment of the present invention.

FIG. 4 is a flow diagram illustrating a method in accordance with oneembodiment of the present invention for generating a model orrepresentation of an abnormal road surface ahead of a moving vehicle.

FIG. 5A shows an example of a temporal occupancy grid generated by theroad surface condition estimating means in accordance with oneembodiment of the present invention, when an abnormal road surfacefeature is relatively farther from the vehicle.

FIG. 5B shows an example of a temporal occupancy grid generated by theroad surface condition estimating means in accordance with theembodiment represented in FIG. 5A, when the abnormal road surfacefeature is closer to the vehicle than in FIG. 5A.

FIG. 5C shows an example of a temporal occupancy grid generated by theroad surface condition estimating means in accordance with theembodiment represented in FIG. 5A, when the abnormal road surfacefeature is closer to the vehicle than in FIG. 5B.

FIG. 6 is a representative example of a composite temporal occupancygrid generated in accordance with one embodiment of the presentinvention.

FIG. 7 is a representative example of a three-dimensional gridconstructed for use in representing the x, y, and z dimensions of anabnormal road feature, in accordance with one embodiment of the presentinvention.

FIG. 8 shows a representation of cells of a three-dimensional gridcontaining cloud points for use in generating a probability densityfunction representing the heights of points on the surface of anabnormal road feature, in accordance with one embodiment of the presentinvention.

FIG. 9 is an illustration of the suspension parameters used incalculating suspension height measurement vector z_(rp) in accordancewith an embodiment of the present invention.

DETAILED DESCRIPTION

FIG. 1 is a schematic diagram of a vehicle control system 12incorporating an active suspension system in accordance with anembodiment of the present invention. Control system 12 includes an arrayof vehicle sensors designed to monitor various vehicle parameters andenvironmental conditions external to the vehicle. The sensor arrayincludes various types of sensors operatively coupled to one or moresystem control modules so as to enable transmission of the sensor inputsto the control module(s). The sensor array may include individualsensors or groups of associated sensors (such as radar, lidar, laserscan, or vision/camera systems) for detecting aspects of the vehicleenvironment and for detecting, for example, a pending collision;inertial sensors (for example, a known or suitable inertial measurementunit (IMU) 22), various wheel speed sensors 14 f, road condition sensors102 if direct measurements of certain road conditions are possible, rainsensors 14 a, suspension height sensors 30, steering wheel angle sensors14 b, steering torque sensors, brake pressure sensors, tire pressuresensors 14 c; sensors (such as a Global Positioning System (GPS) 125)directed to aiding in vehicle location and navigation; cooperativesensors for enabling and facilitating operation of vehicle-to-vehiclecommunication and vehicle-to-infrastructure communication systems (ifany), and other types of sensors. A group of associated sensors (forexample, a road condition sensor suite) may include multiple differenttypes of sensors, depending on the tasks the suite is required toperform in a given control system. In the particular embodiment shown inFIG. 1, the sensor array includes a road condition sensor or a sensorsuite 102 comprising one or more known road condition sensors. The roadcondition sensors may measure such features as, for example, the roadtemperature, whether the road surface is wet or dry, the salinity of anyroad surface moisture, and the presence of snow on the road. The roadcondition sensors may include such elements as laser scanners or camerasto enable visual or digital scanning of a portion of the road surfacebeing traversed by the vehicle.

The control system 12 also includes one or more control modulesoperatively coupled to associated sensors (or groups of sensors), toother control modules, and/or to other elements of the control system.Examples of such control modules include a vehicle dynamics controlmodule (or VDCM) 99 or similar main control module, and control modulesincorporated into various vehicle subsystems, such as a powertraincontrol module 201, a chassis control module 203, and a vehicle occupantrestraint control module 204. In a manner known in the art, the VDCM 99receives inputs from various sensors, processes these inputs inaccordance with a stored control logic or control routine, and generatescontrol signals which are transmitted to various actuatable controlsystem elements or to suitable subordinate or lower level controlmodules (for example, chassis control module 203) which control elementsof an active suspension system (generally designated 210 in FIG. 1)).

While the interactions among all actuatable vehicle systems are ofinterest, the embodiments of the present invention focus primarily onthe active suspension system where such characteristics as suspensiontravel or height, suspension damping, suspension stiffness, andsuspension force are adjustable in real time with actuation responsetimes low enough to enable suspension system control responsive topredicted or actual abnormal road conditions encountered by a vehiclewheel. The suspension actuations are adaptive to the estimated orpredicted road conditions determined using the aforementioned sensingsystems and an associated processing means configured to process datareceived from the sensing systems and determine the type and severity ofthe abnormal road condition.

In a manner known in the art, the various control modules includeprocessing means which receive and process inputs from the associatedsensors or from other elements of the control system (such as othercontrol modules) to generate control signals responsive to the inputs.These control signals are then transmitted to one or more associatedactuatable elements, in a manner known in the art. The actuatablevehicle elements and sub-systems operate responsive to the receivedcontrol signals to control the ride and handling characteristicsassociated with the vehicle. In certain embodiments, the vehicle mayalso incorporate cooperative or interactive communication systems, suchas vehicle-to-vehicle and/or vehicle-to-infrastructure communicationssystems.

The control system 12 also includes various actuatable individualelements and elements of various sub-systems affecting characteristicssuch as ride comfort, handling characteristics, and various safety anddriver assistance features. Examples include elements of the activesuspension system 210, brake control system 212, steering control system214, and their constituent and associated elements.

FIG. 2 is a schematic diagram of one wheel of a vehicle incorporating anactive suspension system of a type which may be controlled using inputfrom an estimator means in accordance with the principles of the presentinvention. As known in the art, an active suspension can be used toimprove ride by adjusting suspension damping and/or spring ratecharacteristics responsive to inputs from the VDCM or other vehiclecontrol modules. In one embodiment, the elements shown in FIG. 2 can beviewed as a single vehicle wheel movable in a vertical direction. Inthis representation, the mass of the vehicle's body is represented bythe sprung mass 11. The wheel, represented by the unsprung mass 13, isattached to the vehicle body 11 by a control arm 15. The body 11 issupported above the unsprung wheel mass 13 by an active suspensionsystem including control arm 15, a spring 19, a damper 21, and a volumeof fluid 17 which acts in series with spring 19 and damper 21. Bycontrolling a fluid flow Q into or out of an actuator 17 (for example, ahydraulic actuator), the suspension forces and ride heights can becontrolled. The wheel's unsprung mass 13 is supported by the roadsurface 23, the tire deflection being represented in FIG. 2 by thespring 25.

A control system incorporating an estimator means as described hereinmay alternatively be used to control other types of actuators andsuspension system elements, for example, the suspension forces can beused for controlling the dynamic normal loading of each wheel.

Referring now to FIG. 1 and also to the schematic block-diagram of FIG.3, embodiments of the vehicle control system described hereinincorporate a road surface condition estimator means, generallydesignated as 100. In one embodiment, an estimator means 100 inaccordance with the present invention comprises a micro process 112 andone or more integration means 110 operatively coupled to themicro-processor system and usable for integrating inputs received fromvarious vehicle sensors and/or other systems.

One or more elements of the estimator means may be incorporated into theVDCM 99 or another control module. Alternatively, elements of theestimator means 100 may be incorporated into an estimator moduleoperatively coupled to the VDCM for interaction with the VDCM and/orwith other control module(s). Such a module may be configured forincorporation into the control system of a new vehicle duringfabrication, or the module may be configured for retrofitting into thecontrol system of an existing vehicle.

In one embodiment, pertinent elements of the estimator means (such asthe controller, any pertinent active suspension system componentsactuatable by the controller, any required sensors, and any othernecessary elements) are installed as replacements for existingcorresponding passive suspension system components.

In another embodiment, the active suspension components are installed soas to function in parallel with the passive suspension systemcomponents. The active suspension components and sensors are coupled toa controller as described for executing the model generation andsuspension control functions. In one particular embodiment, thecontroller may be configured to exercise active control of theactuatable suspension system elements only when an abnormal road surfacecondition is encountered. During normal road conditions, the estimatormeans and its associated active suspension system elements and sensorsmay remain inactive.

In another embodiment, in an existing active suspension system, a newcontroller, configured for processing the sensor data and generatingcontrol commands responsive to detection of an abnormal road surfacecondition, may be installed as a replacement for an existing systemcontroller. The new controller may also be configured for controllingthe active suspension system under normal road conditions, and forperforming the other control functions of the previous controller.

In another embodiment, in an existing active suspension system, a newcontroller may be operatively coupled to existing sensors and/oractuatable suspension system elements. The new controller would also beadapted to operate in conjunction with an existing controller. Suitablecommunications and control protocols would be incorporated into one orboth controllers enabling the new controller to assume suspension systemcontrol when an abnormal road condition is encountered. In all othercircumstances, the first controller would perform suspension systemcontrol functions.

In sum, in any of the embodiments described above, any sensors,controllers or actuatable suspension system elements necessary forexecution of controller commands generated responsive to detection of(and/or contact with) the abnormal road condition may be added onto thevehicle and operatively coupled to existing elements of the vehicle.

Embodiments of the estimator means described herein also incorporate (orutilize data provided by) one or more road condition sensors usable forpreviewing or surveying the road surface to locate abnormal roadconditions (for example, rough patches, potholes, debris, bumps, andother irregularities on the road surface) at a specific set of GPScoordinates ahead of the vehicle, and for estimating variouscharacteristics of the abnormal road surface. For purposes of estimatingthe road condition, one or more sensors designed to provide data to theestimator means may be added or retrofit to an existing vehicle controlsystem. Alternatively, instead of adding a sensor to the vehicle for theestimator means, data from one or more existing vehicle sensors may beprovided to the estimator means for processing.

In one embodiment, the road condition sensors are incorporated into afirst sensor means 103. In one particular embodiment, first sensor means103 includes a known laser scanner 20 incorporated into (or operativelycoupled to) road condition sensor suite 102. Scanner 20 is configuredfor scanning a road surface ahead of the vehicle when the vehicle ismoving, in a manner known in the art. Scanner 20 is configured to scanthe road surface ahead of the vehicle to gather data usable in a mannerdescribed below for generating a point cloud representing anirregularity or abnormality on the road surface (for example, anabnormal level of roughness on the road surface, a pothole, debris, or abump). The first sensor means 102 may also include additional sensorelements as required for a particular application. In addition, aspreviously described, the first sensor means 103 may also includeindividual sensors or groups of associated sensors (such as radar,lidar, laser scan, or vision/camera systems) for detecting variousaspects of the vehicle environment.

In a particular embodiment, a second sensor means 104 includes a knownor suitable IMU 22 incorporated into the vehicle control system 12 forproviding angular velocity and linear acceleration data to theintegration means. As is known in the art, the IMU 22 may includesensors configured for detecting the vehicle's roll rate γ, yaw rate,pitch rate ψ, longitudinal acceleration, lateral acceleration, andvertical acceleration. The second sensor means 104 may also includeadditional sensor elements as required for a particular application.

The various sensor means whose inputs are used in detecting the abnormalroad condition and in predicting the severity of the condition may alsoinclude sensor elements which are incorporated into one of the standardvehicle sensor arrays and/or the elements not normally incorporated intoone of the standard vehicle sensor arrays, depending on the particulartype(s) of sensor data to be used in generating and refining the roadsurface model.

If required, additional means (for example, one or more filters or otherelectronic pre-processing means (not shown)) may be provided forfiltering or otherwise pre-processing the signals from any of the sensormeans prior to processing by the integration means and/or forpre-processing the signals from the integration means prior toprocessing by the micro-processor system.

In one embodiment, an integration means 110 is operatively coupled toone or more of the road condition sensors 102 previously described. Theintegration means 110 is also operatively coupled to the vehicle GPSsystem 125, to the IMU 22, and to the vehicle wheel speed sensors(generally designated 105).

The integration means 110 integrates the data from the road conditionsensors, the GPS system, the wheel speed sensors, and the IMU in a knownmanner to generate a series of six-dimensional cloud vectors asdescribed below, each vector relating to a corresponding cloud pointrepresenting a point on the abnormal road surface. Integration means 110may be operatively coupled to a computer 112 as shown in FIG. 3.Alternatively, the integration means may be incorporated into thecomputer 112. In one embodiment, the integration means comprises afilter (for example, a Kalman filter) suitable for performing therequired integration. Such a filter is described in [1] Stavens D, ThrunS (2006) A Self-supervised Terrain Roughness Estimator for Off-roadAutonomous Driving. In: Conference on Uncertainty in Al (UAI),Cambridge, Mass., the contents of which are incorporated herein byreference.

In one embodiment, computer 112 is incorporated into VDCM 99, as shownin FIG. 1. However, the computer 112 may alternatively be separate fromand operatively coupled to the VDCM, as shown in FIG. 3. Computer 112 isoperatively coupled to the integration means 110 and (if required) tovarious sensors of the vehicle sensor array. Computer 112 receives cloudvector information generated by integration means 110 and performsweighting and/or any other processing of cloud vector information andsensor information required for generation of a prediction or estimateof the condition of a portion of abnormal road surface ahead of thevehicle.

The computer 112 is also configured to process sensor data received fromthe suspension height sensors 30 when the vehicle wheels encounter theabnormal road surface feature previously scanned by the laser scanner.This data is processed in a manner described below to generate a roadcondition vector w_(ROAD) for use in classifying the abnormal roadcondition according to a set of predetermined road conditionclassifications. Responsive to the road condition classification, one ormore control commands may be generated to actuatable elements of theactive suspension system, for controlling the suspension systemresponsive to the estimated road surface conditions.

The computer may also include (or be operatively associated with) amemory (not shown) for storing road surface condition informationcorrelated with the GPS position of the stored road surface condition,along with any other required data and/or information.

A computer 112 configured for performing functions related to generationof the abnormal road condition model (including processing andevaluation of sensor data, generation of control commands responsive toabnormal road conditions predicted by the model based on processing ofthe data, and other model-related functions and functions describedherein) may be incorporated into the main vehicle VDCM 99.

Alternatively, the control routines necessary for evaluating the sensordata and generating control commands responsive to the abnormal roadconditions may be incorporated into a computer of a separate VDCM whichmay replace a pre-existing vehicle VDCM.

Alternatively, the computer may be incorporated into a separate VDCMwhich may be retrofitted to or added on to an existing vehicle, inaddition to the pre-existing vehicle VDCM. Such an add-on VDCM mayincorporate protocols enabling the new VDCM to interface with thepre-existing VDCM. For example, such protocols provide for and governcontrol of various actuatable active suspension system elements by thenew VDCM rather than the pre-existing vehicle VDCM in the event that anabnormal road condition is encountered. In this case, the new VDCM wouldassume temporary control of the actuatable suspension system elementsdeemed necessary to respond to the abnormal road condition, to implementthe necessary control commands. After the vehicle has passed theabnormal stretch of road, control may then be returned to thepre-existing vehicle VDCM.

In embodiments of the vehicle control system described herein, theactuatable vehicle elements which may be controlled by commandsgenerated responsive to inputs from the estimator means includesuspension stiffness adjustment means 210 a, suspension heightadjustment means 210 b, suspension damping adjustment means 210 c,anti-roll adjustment means 210 d, and any other known or suitableactuatable suspension system components affecting vehicle suspensionforces, suspension rattle spaces, the damping components of thesuspensions, the stiffness components of the suspensions, the anti-rollcomponents of the suspensions, suspension travel and/or suspensionheight. Additional vehicle systems or elements may also be configured toactuate responsive to the generated control commands, if desired.

FIG. 4 is a flow diagram illustrating the process flow for one method ofgenerating a model characterizing abnormal road conditions usinggathered sensor data, and for generating suspension control commandsresponsive to the characterized abnormal road conditions. Severalterrain learning and estimation methods have been developed tofacilitate modeling of terrain roughness. Some examples of such methodsare described in the following references: [1] Stavens D, Thrun S (2006)A Self-supervised Terrain Roughness Estimator for Off-road AutonomousDriving. In: Conference on Uncertainty in Al (UAI), Cambridge, Mass.;[2] Brooks C A, Iagnemma K D (2007) Self-Supervised Classification forPlanetary Rover Terrain Sensing. In: 2007 IEEE Aerospace Conference,IEEE. Big Sky, Mont.; and [3] Katz R. Nieto J, Nebot E (2008)Probabilistic Scheme for Laser Based Motion Detection. In: IEEE/RSJInternational Conference on Intelligent Robots and Systems, IEEE, Nice,France, pp 161-166. The teachings of these references are incorporatedherein by reference.

As previously described, one embodiment of the road condition sensorarray 102 includes a laser scanner 20 for scanning road surfaces aheadof the vehicle to obtain road surface data.

In step 300, following the approach described in reference [1], the roadsurface ahead of the vehicle is previewed by laser scanning as thevehicle moves. This provides data used to generate a “preview” orestimate of the road condition ahead of the vehicle. In one embodiment,the laser scanner used is capable of acquiring range data for 100-200angular positions at 50-100 Hz with 0.5 degree angular resolution. Forthis embodiment, a scanner is selected that is operable or configurableto operate at least within these parameters. In addition, additionaldata in the form of estimated GPS coordinates of the scanned roadfeature is gathered by GPS system 106. Also, vehicle roll, pitch and yawdata is obtained from the vehicle inertial sensors of the IMU at thetime at which the abnormal road feature is scanned, and data from thevehicle wheel speed sensors is gathered at the time at which theabnormal road feature is scanned. Data from these various sources isgathered simultaneously so that the laser scan data, IMU data, GPScoordinate estimates and wheel speed data can be time-correlated.

In step 308, following the approach described in reference [1],integration means 110 is used to integrate the laser scan cloud pointdata for each measured point on the abnormal road surface with theestimated GPS location data relating to that point, the time derivativesof the vehicle roll and pitch rates as estimated from IMU data, and thewheel speed sensor data. Using the gathered data, the integration meansgenerates a series of six-dimensional cloud vectors L, each vectorrelating to a corresponding measured cloud point. Each vector L_(i)includes the following elements:L _(i) =[x,y,z,dγ,dψ,t]  (1)where t is the time of measurement of the x, y, and z coordinates of agiven cloud point, and also the time of measurement of γ and ψ; dγ isthe first derivative of the vehicle's roll rate γ at the time ofmeasurement t, as determined from IMU data; dψ is the first derivativeof vehicle's estimated pitch rate ψ at the time of measurement t, asdetermined from IMU data; and x, y, and z are estimated GPS positioncoordinates of the given cloud point at the time of measurement t. Thus,the aggregation of cloud vectors provides a representation of the shapeand dimensions of the road surface and features thereof as measured attime t. The results of the integration are passed to computer 112.

The method described in Reference [1] uses measures shock values tolearn the values of unknown parameters p_(i)'s for improving theaccuracy of the road surface classification. The method of Reference [1]only provides a classification of road roughness. It does not processsuspension height sensor measurements as described herein with regard tothe embodiments of the present invention, and does not predict orpreview the road condition ahead of the vehicle as described herein. Inthe embodiments of the present invention, an alternative sensor fusionapproach is used. This approach uses the laser cloud data in combinationwith the suspension height sensor measurements to generate and refine amodel usable for predicting or estimating the road surface ahead of thevehicle. Road surface condition estimates derived from this model maythen be used to control the vehicle active suspension system prior tocontact between the vehicle and any abnormal road surface featuresrevealed by the laser scans, for mitigating the effects of such contacton the vehicle and its occupants.

In step 309, a temporal occupancy grid is generated.

The method used for estimating or modeling the abnormal road surfacefeature in accordance with embodiments of the present invention is basedon the use of a temporal occupancy grid, successive iterations of whichare designated as 900 a-900 c in FIGS. 5A-5C. Referring to FIGS. 5A-5C,a temporal occupancy grid is imposed on the laser cloud. The gridincludes representations of scanned road features residing within afixed distance in front of the vehicle (for example, road feature 990).The grid moves with the vehicle and the cloud, and is continuouslyupdated with every new laser scan. Thus, as the grid moves with thevehicle and the cloud toward any abnormal road feature (for example,abnormal road feature 990) identified by the scans, and as the grid iscontinuously updated with every new laser scan, the static abnormal roadfeature 990 gets closer to the vehicle.

Grid references x1 includes representations of road features relativelycloser to the vehicle (out to a distance of d1 from the vehicle), whilegrid references x6 include representations of road features relativelyfarther from the vehicle (out to a distance of d2 from the vehicle).FIG. 5A shows the representation of feature 990 when the feature is n−Ntime periods away from physical contact with a vehicle wheel. Theprogression in resolution of the abnormal road surface feature 990 atsuccessive time intervals (n−N), (n−N+1), . . . , (n−1) as the movingvehicle approaches the road surface feature is shown in FIGS. 5A-5C,where FIG. 5A shows an embodiment 900 a of the grid when the abnormalroad surface feature 990 is relatively farther from the vehicle (at timen−N), FIG. 5B shows an embodiment 900 b of the grid (at time n−N+1) whenthe abnormal road feature is closer to the vehicle than in FIG. 5A, andFIG. 5C shows an embodiment 900 c of the grid (at time n−1) when theabnormal road feature is closer to the vehicle than in FIG. 5C.

With the passage of time and every update of the cloud by successivescans, the vehicle draws closer to the abnormal road surface feature,the characteristics of the feature become more clearly defined and theuncertainty relating to the cloud vector representation of the featureis reduced. As the vehicle moves toward the abnormal road feature, theobject moves along the x axis of the grid relative to the vehicle, andthe cloud vectors are continually updated. At the n^(th) time instantthe estimate of the abnormal road surface feature characteristicsaccording to the cloud vectors should coincide with measurements ofcorresponding characteristics of the feature obtainable by the sensordata, from physical contact between the sensors and the road feature.

FIG. 6 shows (in a single view) the time-lapse progression of FIGS.5A-5C as the vehicle approaches the feature 990, and as therepresentation of the road feature 990 becomes more and more distinctand accurate. At the same time, and in a similar manner, as the vehicleapproaches abnormal road features located father from the vehicle thanfeature 990, the representations of these road features become more andmore distinct and accurate.

In step 310, cloud vectors usable for a baseline or reference (i.e.,normal) road surface representation are identified.

In order to identify a change of the road height ahead of the vehicle a3D grid is imposed on the (x, y, z) laser cloud of eligible 6D vectors,in accordance with the following relationship:E=[x,y,z,roll rate γ,pitch rate ψ,time of measurement t]The eligible vectors E are those that are obtained during normal driving(i.e. vectors having relatively lower pitch rate and roll ratecomponents, and excluding roll and pitch rate data acquired duringrelatively severe or violent dynamic maneuvers, which produce relativelyhigh absolute values of γ and ψ). The eligible vectors E satisfy therelationship:

$\begin{matrix}{E = \left\{ {L:{\left( {\gamma^{2} + \psi^{2}} \right) < {\delta }}} \right\}} & \left( {1\; A} \right)\end{matrix}$where δ is a predetermined threshold value dependent upon certaindriving conditions. For example, one factor affecting the quantity δ isvehicle speed; the higher the speed, the larger the value of δ. δ can bedefined as a function of velocity according to the relation:δ=a+bVwhere a and b are two calibrated parameters. These criteria helpdistinguish the baseline or normal road surfaces from the abnormal roadsurface features.

Eligible vectors E may be determined from the same scans of the roadahead of the vehicle which provide information relating to the abnormalroad portions. Alternatively, eligible vectors E may be obtained fromstored or previously acquired information relating to the road surfaceat the given GPS location. In yet another embodiment, the eligiblevectors E are provided by another vehicle which has previously traversedthe portion of road at the given GPS location.

In step 312, a probability density function (PDF) is computed for theroad portion represented in the temporal occupancy grid. The probabilitydensity function is used in calculating and estimated road surfaceheight at each location x,y within the temporal occupancy grid.

Referring to FIG. 7, the third dimension of the grid is defined by therange of z-axis measurements of the laser scanner. For simplicity ofnotation assume a uniformly partitioned N by N by N grid with intervalscentered at the cloud points (x_(oi), y_(oj), z_(ok)). Each cell of thegrid is defined by the triple (x_(i), y_(j), z_(k)), i, j, k={1, N}. Aprobability density function (PDF) of the abnormal road portion heightpredicted at a time n−N (i.e., at n−N time periods before a vehiclewheel physically contacts the abnormal road feature 990 and theresultant suspension height sensor measurements are acquired) iscalculated from the number or frequency S of the cloud points in each ofcells (x_(i), y_(j), z₁), (x_(i), y_(j), z₂), . . . , (x_(i), y_(j),z_(M)) as represented in FIG. 8. The probability density functionP_(ijk) can be determined using the following relation:P _(ijk)(n−N)=S _(ijk)(n−N)/Σ_(k) S _(ijk)(n−N)  (2)

Also in step 312 or at some other point prior to entering the loopdefined by steps 314 through 322, a series of correction factors C_(i),i={1, . . . , N} are initialized at a common non-zero reference value.The correction factors are then updated as described below responsive tocontact between the abnormal road surface and the vehicle wheels. Thecorrection factors are applied to the road height estimates H_(ij)(n−N)previously generated from the laser scan data to produce revisedestimated road height estimates.

In step 314, the estimated road surface height at each location x,ywithin the temporal occupancy grid is calculated. The height H_(ij) ofthe road at a given location (x_(i), y_(i)) is estimated by therelation:

H_(ij)(n − N) = Σ_(k)P_(ijk)(n − N)z_(ok)

In step 316, when the vehicle encounters the scanned road feature (afterpassage of time period n−1 as shown in FIG. 6) and the wheels interactwith the abnormal road feature, data from vehicle sensors (such as thesuspension height sensors) is passed through filter 555 and then tocomputer 112 for processing. In the manner described below, the filtereddata is processed to calculate a value for a road condition vectorw_(ROAD) indicative of an actual road condition at the previouslyscanned location.

In this respect, the portion(s) of the active suspension systemcontacting the abnormal road surface feature act as additional sensorswhich provide information usable for calculating parameters (such asroad profile vector w_(ROAD)) which can be used to control the activesuspension responsive to the abnormal road conditions.

In step 320, the estimated heights h_(wl)′ and h_(wr′) of the roadsurface at the left and right front wheels, respectively, are calculatedusing the estimated road surface heights H and the latest values of thecorrection factors C.

The revised estimated heights h_(wl) and h_(wr) of the road surface atthe left and right front wheels, respectively, at a given location(x_(i), y_(i)) may be obtained by interpolating between the grid centersy_(oj) using inverse distances, and applying correction factors C_(i) tothe previously calculated corresponding road height estimates:

$\begin{matrix}{{h_{wl}^{\prime}\left( {n - N} \right)} = {\Sigma_{i}{\left\{ {C_{i}{H_{iN}\left( {n - N} \right)}{{y_{oi} - y_{wl}}}^{- 1}} \right\}/\Sigma_{i}}{{y_{oi} - y_{wl}}}^{- 1}}} & (4) \\{{h_{wr}^{\prime}\left( {n - N} \right)} = {\Sigma_{i}{\left\{ {C_{i}{H_{iN}\left( {n - N} \right)}{{y_{oi} - y_{wr}}}^{- 1}} \right\}/\Sigma_{i}}{{{y_{oi} - y_{wr}}}^{- 1}.}}} & (5)\end{matrix}$

In step 322, the correction factors are updated. Correction factorsC_(i), i=({1, N}, are continuously updated using a structured learningmethod. The learning method updates the correction factors C_(i), i={1,N} by comparing the road surface heights h_(wl)′ and h_(wr)′

$\begin{matrix}{{h_{wl}^{\prime}(n)} = {\Sigma_{i}{\left\{ {C_{i}{H_{il}(n)}{{y_{oi} - y_{wl}}}^{- 1}} \right\}/\Sigma_{i}}{{y_{oi} - y_{wl}}}^{- 1}}} & (6) \\{{h_{wr}^{\prime}(n)} = {\Sigma_{i}{\left\{ {C_{i}{H_{il}(n)}{{y_{oi} - y_{wr}}}^{- 1}} \right\}/\Sigma_{i}}{{y_{oi} - y_{wr}}}^{- 1}}} & (7)\end{matrix}$as estimated from the laser cloud to the actual measured road surfaceheights h_(wl)(n) and h_(wr)(n) at the same location where:

$\begin{matrix}{{{H_{ij}(n)} = {\Sigma_{k}{P_{ijk}(n)}z_{ok}}},} & (8)\end{matrix}$as given by the relationship described above with respect to step 314.

The probabilities P_(ijk)(n) in relation (8) are calculated from thelinearly weighted frequencies S_(ijk)(n−N), S_(ijk)(n−N+1), . . . ,S_(ijk)(n−1):

     P_(ijk)(n) = S_(ijk)(n)/Σ_(k)(S_(ijk)(n))S_(ijk)(n) = w_(N)S_(ijk)(n − N) + w_(N − 1)S_(ijk)(n − N + 1) + … + w_(l)S_(ijk)(n − 1)where i, j, k={1, N} and weights w₁, w₂, . . . , w_(N) are linearlymonotonically decreasing to reflect the approximately linear dependenceof the error of the cloud estimated height on time, as described inReference [1].

Finally the correction factors are updated by applying the Least MeanSquare (LMS) algorithm to a cost function minimizing the error betweenthe road surface height predicted by the cloud-based estimate and theroad surface height actually measured height at wheels:

$\begin{matrix}{{J(n)} = {\left( {{\Sigma_{i}{\left\{ {C_{i}{H_{il}(n)}{{y_{oi} - y_{wl}}}^{- 1}} \right\}/\Sigma_{i}}{{y_{oi} - y_{wl}}}^{- 1}} - {h_{wl}(n)}} \right)^{2} + \left( {{\Sigma_{i}{\left\{ {C_{i}{H_{il}(n)}{{y_{oi} - y_{wr}}}^{- 1}} \right\}/\Sigma_{i}}{{y_{oi} - y_{wr}}}^{- 1}} - {h_{wr}(n)}} \right)^{2}}} & (11)\end{matrix}$

This procedure yields the following recursion for the vector ofcorrection factors C(n):

$\begin{matrix}{{C\left( {n + 1} \right)} = {{C(n)} + {\alpha\left( {h_{wl}(n)} \right)} - {{C^{T}(n)}{d_{l}(n)}{{d_{l}^{T}(n)}/\left( {{d_{l}^{T}(n)}{d_{l}(n)}} \right)}}}} & (12) \\{{C\left( {n + 1} \right)} = {{C(n)} + {\alpha\left( {h_{wr}(n)} \right)} - {{C^{T}(n)}{d_{r}(n)}{{d_{r}^{T}(n)}/\left( {{d_{r}^{T}(n)}{d_{r}(n)}} \right)}}}} & (13)\end{matrix}$where

d_(il)(n) = H_(il)(n)y_(oi) − y_(wl)⁻¹}/Σ_(i)y_(oi) − y_(wl)⁻¹ − h_(wl)(n)d_(ir)(n) = H_(il)(n)y_(oi) − y_(wl)⁻¹}/Σ_(i)y_(oi) − y_(wl)⁻¹ − h_(wr)(n)and α is a forgetting factor which gives exponentially less weight toprevious, older estimated road surface heights h, J(n) is the costfunction, and, d_(l) ^(T) is a transpose operation of an associatedvector.

The latest (i.e., most recently updated) correction factors are fed backto step 314 to be applied as described above during processing of datarelating to any new abnormal road feature encountered during movement ofthe vehicle. Constant updating of the correction factors applied to thescan data improves the accuracy of the model as more and more data arecollected from interactions between the vehicle suspension and variousabnormal road surface features.

In the manner described above, a method is provided for accuratelyestimating characteristics of the abnormal road surface feature based onlaser scan data, GPS coordinate data, IMU data, and wheel speed sensordata, without the need for vehicle interaction with the road feature. Asadditional abnormal road surface features are encountered, throughrepeated correlation of the laser scan data with the road sensor contactdata, the values of the correction factors C can be refined or tuned.This increases the accuracy and reliability of the road surfacecondition estimates derived from the laser scans. Eventually, theaccuracy of the predictive system road is such that road conditions arepredicted and classified by the estimator using laser scan, GPScoordinate, IMU, and wheel speed sensor data alone, and control of thesuspension may be based solely on the road condition as predicted by theestimator.

In step 318, when data has been gathered from interaction between thesuspension and the abnormal road surface feature, a road profile vectorw_(ROAD) is calculated.

In the manner described below and for another operational mode, thecomputer 112 is also configured to calculate, based on processing of thesuspension height sensor data in a manner described below and using theknown values of various parameters, a value for the road profile vectorw_(ROAD) which reflects the condition of the abnormal road surfacefeature previously scanned by the laser scanner. The road profile vectorw_(ROAD) is a representation of the vertical deviation of the abnormalroad surface from the smooth road surface.

Referring to FIG. 3, in a first step, data from the vehicle heightsensors 30 is transmitted to a suitable filter 555 as the suspensioninteracts with the abnormal road surface. In addition, data from IMU 104relating to body roll γ and body pitch ψ resulting from contact betweenthe vehicle wheels and the abnormal road feature is provided to computer112.

In the next step, a body heave h is measured or calculated. The bodyheave h is defined as the displacement of the center of gravity of thevehicle resulting from contact of the vehicle wheels with the abnormalroad feature. Also, in step 312 a, the body roll γ resulting fromcontact of the vehicle wheels with the abnormal road feature isgathered, and the body pitch ψ resulting from contact of the vehiclewheels with the abnormal road feature is gathered. The parameter h canbe measured or calculated in a known manner from the vehicle heightsensor data generated as the wheel encounters the actual physical roadabnormality, and from the location of the vehicle center of gravity. Theparameters γ and ψ can be gathered from the IMU.

In the next step, the body heave h, the body pitch ψ, and the body rollγ are used to define a body state vector q:

$q = \begin{bmatrix}h \\\gamma \\\psi\end{bmatrix}$

In the next step, an absolute wheel vertical displacement vectorz_(w is) determined. The component of this vector is a wheel's verticaldisplacement resulting from contact with the abnormal road surfacefeature. This can be measured or calculated in a known manner from thesuspension height sensor data generated as the wheel encounters theactual physical road abnormality.

In the next step, a suspension height measurement vector z_(rp) isdetermined. The component of this vector is the relative positiondifference between the two ends of the suspension at each wheel. Thevector z_(rp) can be computed from an absolute wheel verticaldisplacement vector z_(w) and the body state vector q.

Generally, the suspension height measurement vector z_(rp) can beexpressed as a linear function of the body motion and wheel motions bythe following relation:z _(rp) =H ₁ q−H ₂ z _(w)  (4)where H₁ is a 4×3 matrix and H₂ is a 4×4 matrix which can be determinedin a known manner from the suspension geometry and the motion ratios ofa particular suspension system.

The i^(th) component of z_(w) is the vertical displacement of the centerof the i^(th) wheel/tire assembly. The wheel motions are seen at thewheel, suspension height changes are seen along the suspensiondirections which are dynamically varying during motion of the vehicle,and body motions are seen at the directions defined on the body. Theelements of the vehicle experiencing these motions are connected in aknown manner via linkages. Hence the relationships between these motionsmay involve scaling factors and orientation angles in a dynamic fashion.

Basically, the effect of the suspension on vehicle motion depends on theratios of spring displacement to the wheel displacement and springdamper velocity to the wheel velocity. These ratios are known as “motionratios” or “installation ratios”. Since the orientations or axes alongwhich the suspensions act may vary with movement of the suspensioncomponents, the aforementioned “motion ratio” or “installation ratios”are usually not constant. This phenomenon is explained in greater detailby John Dixon, in “Suspension Geometry and Computation”, published byJohn Wiley and Son Ltd., 2009, which is incorporated herein byreference.

Referring to FIG. 9, the suspension height measurement vector z_(rp) fora quarter car model of a vehicle with a strut suspension can becalculated using the following relationship:

$z_{rpi} = {{\frac{l}{m\;{\cos\left( \theta_{i} \right)}}h_{i}} - \frac{z_{wi}}{\cos\left( \theta_{i} \right)}}$where l is the distance from the vehicle center of gravity to therotational center of the wheel/tire assembly, m is the distance from thevehicle center of gravity to where the suspension axis intersecting withthe axle of the wheel and θ is the angle between the direction ofsuspension travel responsive to contact with the abnormal road featureand the direction of motion of the center of the wheel/tire assemblyresponsive to contact with the abnormal road feature.

z_(rp) can also be measured in a manner known in the art using relativeposition sensors mounted on the left-front, right-front, left-rear andright-rear corners of the suspension. Inputs from these sensors can beincorporated into a representative matrix as follows:

$z_{rp} = \begin{bmatrix}z_{{rp}\; 1} \\z_{{rp}\; 2} \\z_{{rp}\; 3} \\z_{{rp}\; 4}\end{bmatrix}$

The passive suspension spring force f_(s) is a function of the relativetravel of the two ends of the spring, which can be computed in a knownmanner from the measured value of z_(rp) in conjunction with certainlinkage ratios.

For example, f_(s)=Kz_(rp) for linear suspensions where K is a gainmatrix. The generic passive suspension spring force f_(s) can beexpressed by the following relationship:

$f_{s} = {\begin{bmatrix}{f_{s\; 1}\left( {L_{s\; 1}z_{rp}} \right)} \\{f_{s\; 2}\left( {L_{s\; 2}z_{rp}} \right)} \\{f_{s\; 3}\left( {L_{s\; 3}z_{rp}} \right)} \\{f_{s\; 4}\left( {L_{s\; 4}z_{rp}} \right)}\end{bmatrix} \equiv {f_{s}\left( {L_{s}z_{rp}} \right)}}$where L_(s) is the matrix determined from the geometry of the suspensionincluding motion ratios.

As known in the art, the passive suspension damper force f_(d) is afunction of the relative velocities of the opposite ends of the damper,which can also be determined from the measured value of vector z_(rp)and can be expressed as:

$f_{d} = {\begin{bmatrix}{f_{d\; 1}\left( {L_{d\; 1}{\overset{.}{z}}_{rp}} \right)} \\{f_{d\; 2}\left( {L_{d\; 2}{\overset{.}{z}}_{rp}} \right)} \\{f_{d\; 3}\left( {L_{d\; 3}{\overset{.}{z}}_{rp}} \right)} \\{f_{d\; 4}\left( {L_{d\; 4}{\overset{.}{z}}_{rp}} \right)}\end{bmatrix} \equiv {f_{d}\left( {L_{d}{\overset{.}{z}}_{rp}} \right)}}$where L_(si) and L_(di) are the corresponding parameters which capturethe motion ratios for the i^(th) wheel and i=1,2,3,4.

A model characterizing the vertical ride dynamics of the vehicle bodycan thus be expressed by the relation:

${M_{bdy}\overset{..}{q}} = {{{- V_{s}}f_{s}} - {V_{d}f_{d}} + F}$where M_(bdy) defines an inertia matrix of vehicle body whichincorporates the sprung mass and the roll and pitch moments of inertia:

${M_{bdy} = \begin{bmatrix}M_{s} & \; & \; \\\; & I_{xx} & \; \\\; & \; & I_{yy}\end{bmatrix}},{V_{s} = \begin{bmatrix}1 & 1 & 1 & 1 \\{- l_{sylf}} & l_{syrf} & {- l_{sylr}} & l_{syrr} \\l_{sxf} & l_{sxf} & {- l_{sxr}} & {- l_{sxr}}\end{bmatrix}},{V_{d} = \begin{bmatrix}1 & 1 & 1 & 1 \\{- l_{dylf}} & l_{dyrf} & {- l_{dylr}} & l_{dyrr} \\l_{dxf} & l_{dxf} & {- l_{dxr}} & {- l_{dxr}}\end{bmatrix}}$where F defines an active suspension force vector whose elements are thesuspension forces at the wheels, where V_(s) is a matrix whose elementsare related to the distances of the suspension springs from the vehiclebody. Also, V_(d) is a matrix whose elements are related to distances ofthe suspension dampers from the vehicle body.

In one embodiment, M_(s) is the sprung mass (vehicle's body mass),I_(xx) is the vehicle body's roll moment of inertia and I_(yy) is thevehicle body's pitch moment of inertia; l_(sylf), l_(syrf), l_(sylr),l_(syrr) are the lateral or perpendicular distances from the left-front,right-front, left-rear, and right rear, suspensions to the longitudinalcenter line of the vehicle; l_(sxlf), l_(sxrf), l_(sxlr), l_(sxrr) arethe longitudinal distances from the left-front, right-front, left-rear,and right rear, suspensions to the center of gravity of the vehicle. Thetire/wheel assemblies obey the following equation of motion whenincorporated into independent, nonlinear suspensions:

${M_{w}{\overset{..}{z}}_{w}} = {{f_{s}\left( {L_{s}z_{rp}} \right)} + {f_{d}\left( {L_{d}{\overset{.}{z}}_{rp}} \right)} - F - {K_{t}z_{td}} - {C_{t}{\overset{.}{z}}_{td}}}$where C_(t) is the damping coefficient of the tire, K_(t) is the tirestiffness and z_(td) is a tire deflection vector whose elements are thetire deflections as the wheels.

An absolute wheel displacement vector z_(w) can be computed for a givenbody state vector q and a given suspension height measurement vectorz_(rp):

z_(w) = H₂⁻¹(H₁q − z_(rp))and a variable w_(ROAD) representing the road profile can now becalculated as:

$\overset{..}{q} = {- {M_{bdy}^{- 1}\left\lbrack {{V_{s}f_{s}} + {V_{d}f_{d}} - F} \right\rbrack}}$z_(td) = (K_(t) + C_(t)s)⁻¹[f_(s) + f_(d) − F − M_(w)H₂⁻¹H_(t)q + M_(w)H₂⁻¹z_(rp)]w_(ROAD) = H₂⁻¹H₁q − H₂⁻¹z_(rp) + z_(td)

A filtered value of the quantity w_(ROAD) is used to characterize theabnormal road surface portion being examined. The quantity w_(ROAD)represents the maximum height of the abnormal road surface feature abovethe normal road surface. In one embodiment, w_(ROAD) is measured inmeters. However, any other suitable unit of measure may be used. In theaggregate, the collection of data points measured along the outersurface of the road feature provides a three-dimensional profile of theroad feature.

The calculated value of w_(ROAD) at a given location may be saved to amemory along with data (for example, GPS coordinates) identifying thelocation of the abnormal road feature. In step 319, and in the mannerdescribed below, this information may be used in classifying theabnormal road surface into one of several predetermined general roadtypes.

In one embodiment of the classification scheme, the abnormal orirregular road conditions can be characterized as one of the followingtypes, based comparisons of the dimensions of the abnormality orirregularity with various vehicle-related dimensions, such as the trackwidth, wheel base, ground clearance and any other pertinent dimensions.For the purposes described herein, a vehicle's track width is defined asthe distance between the center lines of each of the two wheels on thesame axle on any given vehicle.

TABLE 1 No. of Wheels Degree Abnormal in of Road Type Definition ContactSeverity Type 1: Objects abandoned on the roadway. The 1 Small Debrisobjects have width less than the vehicle Medium track width and heightless than the Large vehicle's ground clearance Type 2: Bump with widthgreater than the 2 Small Bump vehicle's track width (e.g., a speedMedium bump) and with height less than the Large vehicle's groundclearance Type 3: Drop on the roadway that has width less 1 SmallPothole than the vehicle's track width and height Medium less than thevehicle's ground clearance Large Type 4: Drop on the roadway with widthgreater 2 Small Drop/ than the vehicle's track width (front or MediumElevation rear axle) or with length greater than the Large vehicle'sbase (left or right side) and with height less than the vehicle's groundclearance. Elevation on the roadway with width greater than thevehicle's track width (front or rear axle) or with length greater thanthe vehicle's base (left or right side) and with height less than thevehicle's ground clearance. Type 5: Small road undulations such as those4 Small Rough shown in unpaved road, gravel, etc. Medium Road Large Type6: Large road undulations or road filled 4 Small Off-road with largerocks, stones, dirt, etc., that Medium have heights close to thevehicle's Large ground clearance Type 7: Large objects on the roadwaywhich has 0 Small Obstacle height greater than the vehicle's groundMedium clearance. Large

If both the estimated road surface conditions derived from the laserscan-based road “preview” and the reactive road type characterizationderived from any previous interactions between the abnormal road featureand the vehicle active suspension system indicate that road conditionsahead of the vehicle are abnormal, the GPS position coordinates (X_(i),Y_(i), Z_(i)) of the abnormality and the current GPS time t_(i) may berecorded together with the quantitative information about the abnormalcondition, where iε{1, 2, . . . , N} and N is the total number ofabnormal road conditions to be tracked. In the manner described below,this information may be used to formulate a digital map showing thelocations of abnormal road features encountered by the vehicle.

For each of the abnormal road types shown in Table 1, the severity ofthe road abnormality may be denoted by the following structured variableas:

T_(map)(X_(i), Y_(i), Z_(i)) ⋅ severity T_(est)(x, y, z) ⋅ severityT(x, y, z) ⋅ severityand the level of severity of the abnormality is defined as either asmall abnormality, medium abnormality, or a large abnormality. Theselevels of abnormality for the road can be, for example, related to thedimensions of the abnormality and/or the dimensions of the abnormalityin relation to various vehicle dimensions, such as wheel base, trackwidth, ground clearance and any other pertinent dimensions.

In one embodiment, degrees of severity within each road type may beassigned by dividing the range of values of w_(ROAD) into three groupsof approximately equal size, and assigning values of w_(ROAD) havingrelatively lower values a “small” degree of severity, assigning valuesof w_(ROAD) having the next highest group of values a “medium” degree ofseverity, and assigning values of w_(ROAD) having the highest group ofvalues a “large” degree of severity.

In an alternative embodiment, the abnormal road type can be classifiedaccording to one of the previously described types and severities basedon the estimated road heights h_(wl)′ and h_(wr)′ at each front wheel,as previously calculated. This enables road condition informationgathered by one or more of the front wheels to possibly be used incontrolling the suspension of the one or more of the rear wheelsfollowing the front wheels, and processing of the road-wheel interactioninformation need only be executed for the front wheel data.

In another embodiment, data from interactions between the abnormal roadfeature and one or more front wheels is averaged with data frominteractions between the abnormal road feature and one or moreassociated rear wheels to generate a composite or average road conditionestimate.

The computer system may be configured for generating and updating such adigital map. In one particular embodiment, the digital map comprises acompilation of road conditions or road states at specified locationsalong the length of the road. The digital model also includes GPScoordinates associated with each stored road condition. The digitalmodel may also include quantitative measures of the severities of theabnormal road conditions. An example of the abnormal road digital mapcan be summarized as in the following table:

TABLE 2 Abnormal Road Digital Map Abnormal Road Type Degree of SeverityGPS Locations T_(map)(X_(i), Y_(i), Z_(i)) = 1 T_(map)(X_(i), Y_(i),Z_(i)) · severity = 1: Small (X_(di), Y_(di), Z_(di)), i = 1, . . . ,n_(ds) Debris T_(map)(X_(i), Y_(i), Z_(i)) · severity = 2: Medium(X_(dj), Y_(dj), Z_(dj)), j = 1, . . . , n_(dm) T_(map)(X_(i), Y_(i),Z_(i)) · severity = 3: Large (X_(dk), Y_(dk), Z_(dk)), i = 1, . . . ,n_(dl) T_(map)(X_(i), Y_(i), Z_(i)) = 2 T_(map)(X_(i), Y_(i), Z_(i)) ·severity = 1: Small (X_(bi), Y_(bi), Z_(bi)), i = 1, . . . , n_(bs) BumpT_(map)(X_(i), Y_(i), Z_(i)) · severity = 2: Medium (X_(bj), Y_(bj),Z_(bj)), j = 1, . . . , n_(bm) T_(map)(X_(i), Y_(i), Z_(i)) · severity =3: Large (X_(bk), Y_(bk), Z_(bk)), i = 1, . . . , n_(bl) T_(map)(X_(i),Y_(i), Z_(i)) = 3 T_(map)(X_(i), Y_(i), Z_(i)) · severity = 1: Small(X_(pi), Y_(pi), Z_(pi)), i = 1, . . . , n_(ps) Pothole T_(map)(X_(i),Y_(i), Z_(i)) · severity = 2: Medium (X_(pj), Y_(pj), Z_(pj)), j = 1, .. . , n_(pm) T_(map)(X_(i), Y_(i), Z_(i)) · severity = 3: Large (X_(pk),Y_(pk), Z_(pk)), i = 1, . . . , n_(pl) T_(map)(X_(i), Y_(i), Z_(i)) = 4T_(map)(X_(i), Y_(i), Z_(i)) · severity = 1: Small (X_(dropi),Y_(dropi), Z_(dropi)), i = 1, . . . , n_(drops) Drop T_(map)(X_(i),Y_(i), Z_(i)) · severity = 2: Medium (X_(dropj), Y_(dropj), Z_(dropj)),j = 1, . . . , n_(dropm) T_(map)(X_(i), Y_(i), Z_(i)) · severity = 3:Large (X_(dropk), Y_(dropk), Z_(dropk)), i = 1, . . . , n_(dropl)T_(map)(X_(i), Y_(i), Z_(i)) = 5 T_(map)(X_(i), Y_(i), Z_(i)) · severity= 1: Small (X_(ri), Y_(ri), Z_(ri)), i = 1, . . . , n_(rs) Rough RoadT_(map)(X_(i), Y_(i), Z_(i)) · severity = 2: Medium (X_(rj), Y_(rj),Z_(rj)), j = 1, . . . , n_(rm) T_(map)(X_(i), Y_(i), Z_(i)) · severity =3: Large (X_(rk), Y_(rk), Z_(rk)), i = 1, . . . , n_(rl) T_(map)(X_(i),Y_(i), Z_(i)) = 6 T_(map)(X_(i), Y_(i), Z_(i)) · severity = 1: Small(X_(oi), Y_(oi), Z_(oi)), i = 1, . . . , n_(os) Off-road T_(map)(X_(i),Y_(i), Z_(i)) · severity = 2: Medium (X_(oj), Y_(oj), Z_(oj)), j = 1, .. . , n_(om) T_(map)(X_(i), Y_(i), Z_(i)) · severity = 3: Large (X_(ok),Y_(ok), Z_(ok)), i = 1, . . . , n_(ol) T_(map)(X_(i), Y_(i), Z_(i)) = 7T_(map)(X_(i), Y_(i), Z_(i)) · severity = 1: Small (X_(obi), Y_(obi),Z_(obi)), i = 1, . . . , n_(obs) Obstacle T_(map)(X_(i), Y_(i), Z_(i)) ·severity = 2: Medium (X_(obj), Y_(obj), Z_(obj)), j = 1, . . . , n_(obm)T_(map)(X_(i), Y_(i), Z_(i)) · severity = 3: Large (X_(obk), Y_(obk),Z_(obk)), i = 1, . . . , n_(obl)

Predicted or generated abnormal road condition information acquired aspreviously described can be used to synchronize with existing abnormalroad digital map or survey data. If the confidence level in theestimated road type is high enough, then the road surface conditionestimate can be used to modify the existing abnormal road digitalmap/survey data or even to generate a new abnormal road type entry inthe digital map that can be saved at memory locations inside a vehicleECU for future use. The estimate can also be used to “reset” existingroad/survey data and/or GPS positions corresponding to the road/surveydata to prevent or compensate for any drifting or error.

The following discussion provides an example of how an existing digitalmap of estimated abnormal road conditions may be modified using thenewly determined road type estimate.

At a future time t, if the GPS system detects that the vehicle (whoselocation at any give moment is represented by GPS coordinates (x, y, z))is approaching an abnormal road condition whose center location has GPScoordinates (X_(i), Y_(i), Z_(i)) and whose road condition has beenestimated or classified in accordance with the above-described method,then the estimated abnormal road type (as determined based on the methodpreviously described) will be compared with the road type on record asapplying to road position (X_(i), Y_(i), Z_(i)).

If the estimated abnormal road type matches the road type on record, theroad type at this location will be maintained as the road type indicatedby the existing map.

If the estimated abnormal road type does not match the road type on theexisting map, the existing digital map will either be modified toreflect the newly determined road type estimate at the given location,or the newly determined road type estimate will be integrated with theexisting road type of record, based on the confidence level of the newlydetermined road type estimate.

The confidence level of road type estimation depends on the quality ofthe data provided by the laser scans and the suspension height sensors.The confidence level may be evaluated and quantified using any ofseveral methods. For example, if the road condition estimate for aparticular abnormal road feature derived from the laser scan data imagecorrelates (to within a predetermined tolerance range) with the roadcondition estimate generated using data from physical interactionsbetween the suspension and the abnormal road surface, then theconfidence level of both road condition estimates may be deemed higher.

Conversely, if the road condition estimate for a particular abnormalroad feature derived from the laser scan data image does not correlate(to within a predetermined tolerance range) with the road conditionestimate generated using data from physical interactions between thesuspension and the abnormal road surface, then the confidence level ofboth road condition estimates may be deemed lower. In instances wherethe confidence level is deemed to be relatively low, actuation of thesuspension system elements responsive to the estimated road conditionsmay be delayed so as to avoid a less-than-optimum response to a possiblyinaccurate road condition estimate.

In another example, if a historical record of road conditions at a givengeographical location exists (for example, from an existing digital mapof a stretch of road), and if any of the road condition estimates forthe same location do not correlate (to within a predetermined tolerancerange) with the historical record, the confidence level for one or bothof the road condition estimates may be set at a medium level.

The above discussion can be implemented using algorithms incorporatingthe following computed variables:

$\begin{matrix}{{i^{*} = {\arg\;{\min\limits_{i}{{\left( {x,y,z} \right) - \left( {X_{i},Y_{i},Z_{i}} \right)}}}}}{h^{*} = {{\left( {x,y,z} \right) - \left( {X_{i},Y_{i},Z_{i}} \right)}}}} & (9)\end{matrix}$where ∥*∥ denotes a vector norm reflecting the distance between the twosets of GPS coordinates, i* indicates the i*th abnormal road featurethat is closest to the current location, h* is the distance between thecurrent location and the i* abnormal road feature.

If the estimated road type at GPS coordinates (x,y,z) is defined at asT_(est)(x,y,z) and the abnormal road type in the existing digital map atlocation (X_(i), Y_(i), Z_(i)) is defined as T_(map)(X_(i), Y_(i),Z_(i)), then T_(est) (x,y,z), T_(map) (X_(i), Y_(i), Z_(i)) and T(x, y,z) all belong to the finite set {0,1,2,3,4,5,6,7}, where 0 correspondsto a normal road condition and i corresponds to the q^(th) abnormal roadtype defined in TABLE 1 for q=1, 2, 3, 4.5, 6, 7.

For a given vehicle velocity v_(x), we define γ(v_(x)) as a velocitydependent scalar. If the computed distance h* in (9) is equal to or lessthan such a scalar, such that:

$\begin{matrix}{h^{*} \leq {\gamma\left( v_{x} \right)}} & (10)\end{matrix}$then the vehicle, whose current location is reflected by the GPScoordinates (x,y,z), is determined to be approaching a road segmenthaving GPS coordinates (X_(i), Y_(i), Z_(i)) and which is currentlycharacterized on the existing digital map as being of road typeT_(map)(X_(i), Y_(i), Z_(i))ε{1,2, 3, 4, 5, 6, 7}.

If the confidence level of the road type estimate T_(est)(x,y,z) islower than a predetermined threshold value (e.g., 40% confidence level),then the calculated road type estimate should be replaced by the i^(th)abnormal road type T_(map)(X_(i), Y_(i), Z_(i)) obtained from thepre-existing abnormal road digital map.

If the estimated road type T_(est)(x,y,z) does not match T_(map)(X_(i),Y_(i), Z_(i)) when relation (10) is satisfied, a fusion or integrationof T_(est)(x,y,z) with T_(map)(X_(i), Y_(i), Z_(i)) may be performed toprovide a final road type T(x,y,z) at the location:T(x,y,z)Ξ[T _(map)(X _(i) ,Y _(i) ,Z _(i)),T _(est)(x,y,z)]where Ξ represents a smoothing strategy, weighted sum strategy or otherknown means for achieving smooth integration between T_(map)(X_(i),Y_(i), Z_(i)) and T_(est)(x,y,z).

If a road condition is encountered where T_(est)(x,y,z) conforms to oneof the road condition types shown in the above table, but there is no i*such that the corresponding h* computed from existing digital mapinformation satisfies relation (10), the following digital map entrywill be initiated and added to the existing digital map:

$\begin{matrix}{{\left( {X_{j},Y_{j},Z_{j}} \right) = \left( {x,y,z} \right)}{{T_{map}\left( {X_{i},Y_{i},Z_{i}} \right)} = {T_{est}\left( {x,y,z} \right)}}} & (13)\end{matrix}$

For each of the abnormal road types shown in the above Table 1, theseverity of the road abnormality is denoted by the following structuredvariable as:

T_(map)(X_(i), Y_(i), Z_(i)) ⋅ severity T_(est)(x, y, z) ⋅ severityT(x, y, z) ⋅ severityand the level of severity of the abnormality is defined as either asmall abnormality, medium abnormality, or a large abnormality. Theselevels of abnormality for the road can be, for example, related to thedimensions of the abnormality and/or the dimensions of the abnormalityin relation to various vehicle dimensions, such as wheel base, trackwidth, ground clearance and any other pertinent dimensions.

In a particular embodiment, first and second vehicles are each equippedwith vehicle-to-vehicle (V2V) communication systems, and the firstvehicle is equipped with components and systems structured for detectingand modeling abnormal road conditions and for acquiring and processingdata relating to the detected abnormal road conditions, in accordancewith the embodiments previously described. In a scenario where the firstvehicle is traveling ahead of the second vehicle, information relatingto the abnormal road condition can be readily communicated from thefirst (leading) vehicle to the second (following) vehicle. If the secondvehicle is also equipped with components and systems structured fordetecting and modeling abnormal road conditions and for acquiring andprocessing data relating to the detected abnormal road conditions inaccordance with the embodiments previously described, the receivedabnormal road condition information can be further processed and/orintegrated with information stored in vehicle memory. Such abnormal roadinformation can also be similarly passed to surrounding vehicles.

In another particular embodiment, a vehicle equipped with theaforementioned sensing and processing systems is also equipped with avehicle-to-infrastructure communication system (enabling communicationfrom the vehicle to, for example, roadside data receiver units that canexchange information with a transportation management or traffic datacenter). Then the abnormal road conditions can be reported to a roadservice crew and can aid the service crew in locating the abnormal roadsegment to facilitate repairs.

If information stored in an infrastructure memory or database indicatesthat abnormal roads having large degrees of severity are farther alongthe vehicle's current route (i.e., outside current sensor detectionrange), the vehicle controller may notify the driver of the pending roadconditions and may also be configured to present one or more alternativeroutes to the driver.

If information stored in an infrastructure memory and/or databaseindicates or information processed by the suspension control systemindicates there are abnormal road conditions ahead of a severity toogreat for the active suspension control system to accommodate withoutrisk of damage to the vehicle, the controller may notify the driver andmay also be configured to present one or more alternative routes to thedriver.

In another particular embodiment, a vehicle equipped with theaforementioned sensing and processing systems is also equipped with awireless or mobile system or device that can communicate with a cloudserver. Then, information relating to the abnormal road conditions maybe transmitted to the cloud and may be accessed by other drivers andvehicles having access to the cloud. If information received from thecloud indicates that higher-severity abnormal roads lie along thecurrent vehicle route, the vehicle controller may notify the driver ofthe pending road conditions and may also be configured to present one ormore alternative routes to the driver. Also, if information receivedfrom the cloud indicates there are abnormal road conditions ahead whichare too severe for the active suspension control system to accommodatewithout unacceptable risk of damage to the vehicle, the vehiclecontroller may notify the driver of the pending road conditions and mayalso be configured to present one or more alternative routes to thedriver.

In addition, other types of abnormal road surface information detectedby the vehicle suspension or by sensors may be communicated to othervehicles, to infrastructure locations, or to the cloud. For example,wheel longitudinal slip and/or vehicle side slip angle may (if outsidepredefined normal parameters) and associated geographical locationinformation be transmitted to other vehicles, to infrastructurelocations, or to the cloud, and then stored for later access andupdating. Then, from the infrastructure locations or to the cloud, thisinformation may then be transmitted to (or may be accessible by)additional vehicles traversing the location or strip of road where theabnormality is located. A driver of the vehicle receiving theinformation may use this information to engage vehicle safety featuresfor mitigating the effects of the abnormal road condition on thevehicle. Alternatively, the vehicle may, upon receipt of the abnormalroad condition information, automatically or autonomously engagerelevant safety features or systems. A driver may also use thisinformation to plan an alternative route around the abnormal roadcondition, or the vehicle systems may (either automatically or at thedirection of the driver) calculate a suitable alternative route.

In step 330, in a particular embodiment, when the abnormal roadcondition and severity have been established, one or more controlcommands to actuatable elements of the active suspension system may begenerated responsive to the road condition and severity, for controllingthe suspension system responsive to the predicted or detected roadsurface conditions.

These control commands may be directed to compensating for the effect ofthe abnormal road condition on the vehicle, thereby minimizing theeffect of the abnormal condition on ride quality. Control signals may begenerated for and directed to elements of the chassis control system orthe active suspension system, for example. Characteristics which may bemodified to compensate for the effects of the abnormal road condition onthe vehicle include the suspension forces, the suspension rattle spaces,the damping components of the suspension system, the stiffnesscomponents of the suspension system, and the anti-roll component of thesuspension systems.

In particular embodiments, the suspension stiffnesses and/or dampingcharacteristics at one or more of the vehicle wheels are adjusted so asto prevent or mitigate negative effects on ride and passenger comfortresulting from contact between the vehicle wheels and the abnormal roadsurface feature. In addition, because the control commands are based ona prediction of the abnormal road surface features or on informationrelating to the actual road feature (gathered, for example, throughprevious interactions between one or more vehicles and the roadfeature), certain control commands may be implemented prior to actualcontact between the vehicle wheels and the abnormal road surfacefeature.

As stated previously, the suspension system control commands can begenerated based on various estimates of the road type. In one example,assume the current vehicle GPS location is (x, y, z) and the final andestimated road type at the location (X, Y, Z) is denoted as T(X, Y,Z)ε{0,1,2,3,4.5,6,7}.

For a normal road condition (i.e., where T(x, y, z)=0), the road isassumed to be a normal road type and conventional smooth road activesuspension control strategies may be applied. Such traditionalsuspension strategies include body control for ride comfort, wheelcontrol for improved wheel road holding, and handling control. WhereT(x, y, z)≠0 any of several approaches may be used. Nontraditionalapproaches are contemplated such that the active suspension system isadaptive to the 7 types of abnormal road conditions shown above. Thatis, cases are considered where:T(x,y,z)ε{0,1,2,3,4.5,6,7}and each type of T(x, y, z) has a different severity of the abnormalitywhich is denoted asT(x,y,z)·severityε{small,medium,large}

The different abnormal road types for T(x, y, z) will require differentactive suspension control strategies. The active suspension controlstrategies may be customized for each road type and its differentseverities of abnormality.

In one embodiment, the control architecture is similar to a knowndual-loop control approach having an inner loop and an outer loop. Theouter control loop sets the target active suspension forces or thetarget rattle spaces so as to respond to a specific type of roadcondition, based on vehicle level performance requirements. The innercontrol loop regulates the target values set by the outer loop, throughthe adjustment of suspension actuator level controls. The outer controlsloop can be implemented in both feedback and feedforward fashion.

The following describes some task-oriented suspension control modeswhich may be implemented responsive to a given set of abnormal roadconditions. Such modes include diagonal rolling control, three-wheelsupport control, zigzag maneuver control, alternating damping control,alternating stiffness control, and jumping control.

The timing and severity or magnitude of the generated control responsesmay be dependent on the type and severity of the abnormal roadcondition, vehicle performance requirements, the estimated time untilthe vehicle reaches or contacts the abnormal road feature, and otherpertinent factors.

Diagonal Rolling Control (DRC)

Diagonal Rolling Control (DRC) is an open-loop control scheme where thesuspension forces provided by two wheel suspensions in first diagonallocations are adjusted so as to provide relatively larger suspensionforces or increased dampening or stiffness, while the suspension forcesprovided by two wheel suspensions on second, opposite diagonal locationsare adjusted so as to provide relatively smaller suspension forces ordecreased dampening or stiffness. Such a suspension control mode allowsthe vehicle to roll about an axis extending through the first diagonallocations when a wheel in one of the second diagonal locations isdisturbed by interaction with an abnormal road surface. This aids inreducing vehicle rolling and the shock to the vehicle. The magnitudes ofthe suspension forces provided by the second diagonal wheel suspensionsmay be automatically adjusted by a controller in relation to themagnitude of the calculated value of the road profile vector w_(ROAD),such that the greater the road profile vector value magnitude, the lowerthe suspension forces in the second diagonal wheel suspensions. Thisenables the wheel suspensions at the second diagonal wheel locations toadapt to a wide variety of values of w_(ROAD). Correlations between themagnitude of the calculated value of the road profile vector w_(ROAD)and the appropriate associated dampening or stiffness values at thesecond diagonal wheel suspensions may be determined by suspensiontesting prior to vehicle operation and stored in the controller or in asuitable memory operatively coupled to the controller.

DRC can also be utilized to increase the rattle spaces of the twosuspensions along the first diagonal locations and decrease the rattlespaces of the two wheel suspensions along the second diagonal locations,thereby enabling the vehicle to handle a relatively larger disturbancemet by a wheel in one of the second diagonal locations. In this way awheel in one of the second diagonal locations can be lifted relativelyeasily due to rolling of the vehicle about the first diagonal axis. Thisfeature, combined with use of the rattle space inherent in the wheellocated at the one of the second diagonal locations, enables this wheelto climb over a larger obstacle.

Thus, DRC can combine suspension force-based “diagonal adjustment” withsuspension rattle space-based diagonal adjustment as described above,thereby enhancing control capability.

Three Wheel Support (TWS)

Three Wheel Support (TWS) is a suspension control scheme wherein threewheels have relatively “hard” suspension settings that can support thevehicle body while the fourth wheel has a relatively “slack” suspensionsetting. Thus, the fourth wheel is free to elevate relatively higher(and more easily) from the ground. TWS can be applied throughappropriate variation of suspension forces or rattle spaces, or througha combination of both methods.

The magnitude of the suspension force applied to the fourth wheel toprovide the necessary “slack” may be automatically adjusted by acontroller in relation to the magnitude of the calculated value of theroad profile vector w_(ROAD), such that the greater the road profilevector value magnitude, the lower the suspension force in the fourthwheel suspension. This enables the wheel suspensions at the seconddiagonal wheel locations to adapt to a wide variety of values ofw_(ROAD). Correlations between the magnitude of the calculated value ofthe road profile vector w_(ROAD) and the appropriate associateddampening or stiffness values at the fourth wheel suspension may bedetermined by suspension testing prior to vehicle operation and storedin the controller or in a suitable memory operatively coupled to thecontroller. Advanced control/optimization methods (such as modelpredictive control, adaptive control, fuzzy control, etc.) can also beused as known in the art to further optimize the vehicle systemresponses.

Zigzag Maneuver Control (ZMC)

ZMC is an open loop suspension control scheme where the vehicle conductsa zigzag or “snake” maneuver to raise one front wheel at a time so as toclimb over obstacles. ZMC integrates both steering control andsuspension control to achieve the desired effect. The controlledsteering system lifts a first front wheel by making that wheel an insidewheel through a short, sharp turn in the wheel's direction, while at thesame time using the active suspension at the lifted first wheel tofurther raise the wheel. After the first turn is complete and the firstwheel has climbed over an object, the steering control steers thevehicle sharply in the opposite direction to make the second front wheelan inside wheel to raise it, while at the same time the controlledsuspension for the second wheel further raises this wheel. The angles towhich the vehicle wheels are turned to execute the control commands maybe calculated by the controller using such factors as the vehicle speed,the characteristics of the road surface abnormality, pertinent vehicledimensions such as track width and ground clearance, and other pertinentfactors.

Several methods and systems exist which are capable of controlling thesteering in the manner required. Several of these systems use apply an“overlay” or modification to an existing steering wheel angle byapplying a torque required to turn the wheel from the existing angle toa known, desired angle. One example of such a system is disclosed inU.S. Pat. No. 6,854,558, which is incorporated herein by reference inits entirety. Advanced control/optimization methods (such as modelpredictive control, adaptive control, fuzzy control, etc.) can also beused as known in the art to further optimize the vehicle systemresponses.

Alternating Damping Control (ADC)

ADC is an open loop suspension control scheme where the suspensiondamping is adjusted to one setting just before an event (e.g., such asthe wheel supported by the suspension encountering a speed bump) and isswitched to another setting just after the event occurs.

Alternating Stiffness Control (ASC)

ASC is an open loop suspension control scheme where the suspensionstiffness is adjusted to one setting just before an event and isswitched to another setting after the event occurs, where the adjustingamount is related to the events.

The suspension stiffness can also be constantly adjusted for the vehicleto better adapt to the road.

Jumping Control (JC)

JC is an open loop suspension control scheme where the suspension isadjusted to generate a periodic heave and pitch motion that can causethe vehicle behave as if it will “jump”, for example, over a road bump.

The following illustrates several examples of how the control schemesdescribed above may be applied to actuate elements of the activesuspension system for improving the vehicle's handling, ride comfort,and safety when one or more wheels of the vehicle encounter one of theabnormal road conditions previously described. In the followingscenarios, it is assumed to be certain or highly likely that the vehiclewill encounter the abnormal road condition because, for example, thereare no steerable paths around the abnormal road condition, or becausethe vehicle is travelling at a relatively high speed and the driver isunlikely to be able to respond to the abnormal road condition properlyand/or in a timely fashion.

Active Suspension Adaptations Responsive to Rodd Condition Type 1(Debris)

In this case, T(x, y, z)=1 and one wheel of the vehicle will encounter arelatively small piece of debris whose dimensions are larger than thoseencountered under normal road conditions where T(x, y, z)=0. In thiscase, the debris has dimensions less than the vehicle's track width,base, and ground clearance. If the estimating means predicts T(x, y,z)=1 and T(x, y, z)·severity=small, the debris can be run over by thevehicle wheel without causing significant harm to the vehicle. The wheelsuspensions spaced apart from the debris to one side of the debris canbe adjusted to adapt to the debris. More specifically, these suspensionscan be controlled to minimize the effect of the impact with the debris,to maximize driving comfort, and to optimize vehicle responseimmediately after passing the debris. Such optimization can be conductedthrough using such tools as model predictive control (MPG), adaptivecontrol, fuzzy control, etc.

If the estimator means predicts T(x, y, z)=1 and T(x, y,z)·severity=medium for an abnormal section of the road, DRC control canbe initiated such that the medium-size debris causes the vehicle to rollabout an axis extending through second diagonal wheel locations, aspreviously described, responsive to contact with an impinging wheellocated at one of the first diagonal wheel locations. The contactingwheel can also be elevated prior to contact with the debris so as toreduce the severity of the contact effects.

If the estimator means predicts T(x, y, z)=1 and T(x, y,z)·severity=large, the ZMC mode can be initiated such that wheel contactwith the debris can be avoided, or the severity of the contact effectsare reduced. If the ZMC mode is not viable due to a lack of spacesurrounding the abnormal road condition, the ADC mode can be implementedsuch that suspension damping of the wheel contacting the debris will bereduced just before contacting the debris and increased just aftercontacting the debris. Advanced control/optimization methods (such asmodel predictive control, adaptive control, fuzzy control, etc.) canalso be used as known in the art to further optimize the vehicle systemresponses.

Active Suspension Adaptations Responsive to Road Condition Type 2 (Bump)

In this case, T(x, y, z)=2, and two front wheels are likely tosimultaneously contact a bump that has a width greater than thevehicle's track width, a length less than the vehicle's base, and aheight less than the vehicle's ground clearance. Some time after thefront wheels pass the bump, the two rear wheels will meet the bump. Ifthe estimator means predicts T(x, y, z)=2 and T(x, y, z)·severity=small,then the bump can be run over without causing significant harm to thevehicle. In this case, the vehicle suspension system elements will beadjusted to optimize the vehicle's dynamic response and ride comfort. Inaddition, optimization of the vehicle's step or pulse response may beimplemented. The optimizations can be conducted through using such toolsas Model Predictive Control, adaptive control, fuzzy control, etc.

If the estimator means predicts T(x, y, z)=2 and T(x, y,z)·severiry=medium, the ADC and ASC modes will be initiated. Forinstance, right before the wheel contacts the bump, the front wheelactive suspension elements will be adjusted to increase the effectivestiffness of the suspension, to aid in maintaining a good groundclearance. The front wheel active suspension elements will also beadjusted to decrease the effective damping of the suspensions, to reducethe shock effect of wheel impact. Immediately after the front wheelspass the bump, the front suspensions will be adjusted to increase theireffective damping so that the vehicle's vibrations due to the impact canbe damped out relatively quickly. The rear suspension controls will beadjusted in the same manner.

If the estimator means predicts T(x, y, z)=2 and T(x, y,z)·severity=large, and the current vehicle speed is above apredetermined threshold value, the vehicle speed will be reduced to apredetermined limit. The suspension adjustment will implement one of theZMC or JC control modes in an attempt to at least partially mitigate theforces generated by contact with the bump. In this case, the ZMC modeemploys a small, sharp turn in one direction to lift one wheel over thebump and then another such turn in the opposite direction to lift theother wheel over the bump. At the same time, the suspension may employsuspension height management means to further raise the wheels

Active Suspension Adaptations Responsive to Road Condition Type 3(Pothole)

In this case, T(x, y, z)=3, and one wheel will encounter a hollow areahaving a width less than the vehicle's track width, a length less thanthe vehicle's base, and a depth less than the vehicle's groundclearance. If the estimator means predicts T(x, y, z)=3 and T(x, y,z)·severity=small, actuation of the front left and right suspensionelements will be coordinated to increase the roll stiffness couplingand/or to optimize the suspension responses. After the front wheels passthe pothole, actuation of the rear left and right suspensions will becoordinated to increase the roll stiffness coupling and/or optimize therear suspension responses. If the estimating means predicts T(x, y, z)=3and T(x, y, z)·severity=(medium or large), suspension control will beintegrated with the SM control mode to temporarily lift the wheel overthe pothole. Alternatively, the TWS mode will be implemented such thatthe three wheels maintain road contact and the 4th wheel is lifted overthe pothole. Advanced control/optimization methods (such as modelpredictive control, adaptive control, fuzzy control, etc.) can be usedto further optimize the vehicle system responses.

Active Suspension Adaptations Responsive to Road Condition Type 4 (Drop)

In this case, T(x, y, z)=4, and there is a dip in the road surfacehaving either a width larger than the vehicle's track width (a lateralroad drop) or having a narrow width but a length larger than thevehicle's base (a longitudinal road drop, for example a road edge). Inthe lateral road drop case, the front two wheels will sink into the dropfollowed by the rear two wheels. In the road edge case, the wheels alongone side of the vehicle will sink into the drop. If the estimator meanspredicts T(x, y, z)=4 for a lateral road drop with T(x, y,z)·severity=small, the suspension control will optimize the vehicle'sstep response. If the estimator means predicts T(x, y, z)=4 for alateral road drop with T(x, y, z)·severity=(medium or large), thefollowing control strategies may be implemented. Just before the frontwheels encounter the lateral drop, the front wheel suspensions will beadjusted to increase the suspensions' effective stiffness, to aid inmaintaining sufficient ground clearance. Also, the front wheelsuspensions will be adjusted to decrease the suspensions' effectivedampening to reduce the shock effect of wheel contact after the drop.

In addition, immediately after the front wheels pass the lateral drop,the front suspensions will be adjusted to increase their effectivedamping coefficients so that the vehicle's vibration due to the lateraldrop can be damped out relatively quickly. The rear suspension controlswill be adjusted so as to provide a similar effect. This control schemeis effectively a combination of the ADC and ASC modes. Advancedcontrol/optimization methods (such as MPC, adaptive control, fuzzycontrol, etc.) can be used to further optimize the vehicle systemresponses.

Active Suspension Adaptations Responsive to Road Condition Type 5 (RoughRoad)

In this case, T(x, y, z)=5 and the road surface has a relatively smalllevel of unevenness (produced, for example, by the presence of gravel)which could introduce high frequency disturbances to the vehicle. Uponthe detection of this kind of abnormality, the damping coefficients ofall the wheel suspensions will be increased and the relative stiffnessof all the suspensions will be decreased. As in previous cases,optimization can be conducted through using such tools as modelpredictive control, adaptive control, fuzzy control, etc.

Active Suspension Adaptations Responsive to Road Condition Type 6(Off-Road)

In this case, T(x, y, z)=6 and the off-road terrain ahead of the vehiclehas a medium to large level of unevenness due to rocks, dirt, unpavedsurfaces, etc. Upon the detection of such a condition, the suspensioncontrols will be actuated so as to reduce anti-roll stiffness, therebypermitting better wheel-to-road contact. This acts to improve tractionwhen the vehicle is driven in a straight line and at relatively highspeeds. The suspension controls will also be actuated to increaseanti-roll stiffness whenever the driver initiates an aggressivemaneuver, such as a sharp turn, for example. In addition, ride comfortcriteria will be relaxed to enhance drivability during off-road terraindriving. As before, the optimization can be conducted through using suchtools as model predictive control, adaptive control, fuzzy control, etc.

Active Suspension Adaptations Responsive to Road Condition Type 7(Obstacle)

In this case, T(x, y, z)=7 and the road ahead of the vehicle includes anobject which has a height greater than the vehicle's ground clearance.If there are no steerable paths around the abnormal condition (forexample, due to heavy traffic in adjacent lanes) or where the vehicle isdriven at high speed under conditions where the driver may be unable torespond to adverse conditions properly and in a timely fashion, theactive suspensions' effectiveness is limited and vehicle safety measuressuch as collision mitigation by braking (and/or steering) will beinitiated. In order to better prepare the vehicle for accident avoidanceresponsiveness, the active suspension elements are adjusted according toa predetermined performance control setting or to so as to provide anoverall optimized response, taking into account all the availableactuators.

It will be understood that the foregoing descriptions of embodiments ofthe present invention are for illustrative purposes only. As such, thevarious structural and operational features herein disclosed aresusceptible to a number of modifications commensurate with the abilitiesof one of ordinary skill in the art, none of which departs from thescope of the present invention as defined in the appended claims.

What is claimed is:
 1. A method for generating a representation of aroad surface ahead of a moving vehicle, comprising steps of: a)acquiring road surface data out to a distance d2 ahead of the vehicle ata time n−N and vehicle data time-correlated with the surface data; b)generating, using the data, a plurality of cloud vectors representingthe road surface; and c) generating, using the cloud vectors, a temporaloccupancy grid containing a representation of the road surface out tothe distance d2 at time n−N.
 2. The method of claim 1 further comprisingthe steps of: d) acquiring road surface data out to the distance d2ahead of the vehicle at a time (n−N+1) and vehicle data time-correlatedwith the road surface data; e) updating, using the newly-acquired data,the plurality of cloud vectors representing the road surface up to thedistance d2 ahead of the vehicle; f) updating the temporal occupancygrid using the updated cloud vectors, so that the grid contains arepresentation of the abnormal road surface out to the distance d2 attime (n−N+1) g) incrementing the time by one time period; h) repeatingsteps d) through g) until the time is equal to n.
 3. The method of claim2 further comprising the step of identifying cloud vectors of theplurality of cloud vectors usable for representing a reference portionof the road surface ahead of the vehicle.
 4. The method of claim 3further comprising the step of, using the acquired road surface data,calculating a probability density function for the road surfacerepresented in the temporal occupancy grid.
 5. The method of claim 4further comprising the step of, using the probability density functionand the acquired road surface data, calculating an estimated roadsurface height at a location of each cloud vector of the plurality ofcloud vectors.
 6. The method of claim 5 further comprising the step ofacquiring data vehicle suspension response data resulting from aninteraction between a vehicle suspension and a portion of the roadsurface represented by a corresponding portion of the plurality of cloudvectors.
 7. The method of claim 6 further comprising the step ofinitializing a set of correction factors for application to selectedones of the road surface heights calculated at the locationscorresponding to the plurality of cloud vectors.
 8. The method of claim7 further comprising the step of calculating, using values of thecorrection factors and the selected ones of the road surface heightscalculated at the locations of the plurality of cloud vectors, estimatedheights h_(wl)′ and h_(wr)′ of the road surface at left and right frontwheels of the vehicle.
 9. The method of claim 6 further comprising thestep of calculating, using the vehicle suspension response dataresulting from the interaction between a vehicle suspension and theportion of the road surface, a road profile vector representing avertical deviation of the portion of the road surface from the referenceportion of the road surface.
 10. The method of claim 9 furthercomprising the step of transmitting, to another vehicle, informationrelating to the portion of the road surface.
 11. The method of claim 9further comprising the step of transmitting, to a vehicle-relatedinfrastructure, information relating to the portion of the road surface.12. The method of claim 9 further comprising the step of transmitting,to a cloud server, information relating to the portion of the roadsurface.
 13. A method for controlling an active suspension, comprisingsteps of: determining a dimension of a road abnormality ahead of thevehicle; comparing the dimension with a vehicle dimension; responsive tothe comparison, classifying the abnormality as one type of a pluralityof predetermined types; responsive to a dimension of the abnormality,further classifying the abnormality as having one of a small, medium,and large severity; and controlling the suspension responsive to thetype and severity.
 14. The method of claim 13 wherein the step ofcontrolling the suspension comprises controlling the suspension byimplementing a Diagonal Rolling Control scheme.
 15. The method of claim13 wherein the step of controlling the suspension comprises controllingthe suspension by implementing a Three Wheel Support control scheme. 16.The method of claim 13 wherein the step of controlling the suspensioncomprises controlling the suspension by implementing a Zigzag ManeuverControl (ZMC) scheme.
 17. The method of claim 13 wherein the step ofcontrolling the suspension comprises controlling the suspension byimplementing an Alternating Damping Control scheme.
 18. The method ofclaim 13 wherein the step of controlling the suspension comprisescontrolling the suspension by implementing an Alternating StiffnessControl scheme.
 19. The method of claim 13 wherein the step ofcontrolling the suspension comprises controlling the suspension byimplementing a Jumping Control scheme.
 20. A method for controlling anactive suspension system, comprising the steps of: a) by a computingmeans operatively coupled to the suspension system: initializing atleast one correction factor for use in calculating an estimated heightof a location on the road surface ahead of the vehicle; b) by at leastone road condition sensor operatively coupled to the suspension system,previewing a portion of a road surface ahead of a moving vehicle toacquire road surface condition data; c) by at least one vehicleparameter sensor operatively coupled to the suspension system, acquiringvehicle parameter data that is time-correlated with the road surfacedata; d) by a computing means operatively coupled to the suspensionsystem: generating, using the road surface data, the vehicle parameterdata, and a current value of the at least one correction factor, anestimated height of a location on the road surface ahead of the vehicle;and e) by a controller operatively coupled to the suspension system,controlling the active suspension system responsive to the estimatedheight of the road surface location, when the vehicle is moving on theroad surface.
 21. The method of claim 20 wherein the step of generatingthe estimated height of the location on the road surface comprises thestep of integrating the road surface data with the vehicle parameterdata to generate a plurality of cloud vectors representing an associatedplurality of points on the road surface at the road surface location.22. The method of claim 21 wherein the step of generating the estimatedheight of the location on the road surface further comprises the step ofgenerating, using cloud vectors of the plurality of cloud vectors, atemporal occupancy grid containing a representation of the road surfacelocation.
 23. The method of claim 22 wherein the step of generating theestimated height of the location on the road surface further comprisesthe step of calculating an estimated road surface height at each pointon the road surface within the temporal occupancy grid.
 24. The methodof claim 23 further comprising the step of, by a computing meansoperatively coupled to the suspension system, identifying cloud vectorsfrom the plurality of cloud vectors usable for a reference, normal roadsurface representation.
 25. The method of claim 24 wherein the step ofcalculating an estimated road surface height at each point within thetemporal occupancy grid comprises the step of calculating a probabilitydensity function for the road surface location represented in thetemporal occupancy grid.
 26. The method of claim 20 further comprisingthe steps of: by at least one vehicle parameter sensor operativelycoupled to the suspension system, acquiring data from resulting contactbetween the road surface location and front wheels of the vehicleoperatively coupled to the suspension system; by a computing meansoperatively coupled to the suspension system: calculating, using thecontact data, an estimated road surface height at the road surfacelocation; and updating the at least one correction factor; and repeatingsteps b)-e).